Image processing apparatus

ABSTRACT

An image processing apparatus includes an image processor that performs error diffusion processing by applying an error diffusion matrix to multi-valued image data having pixels two-dimensionally arranged, so as to convert the multi-valued image data into binary image data. The binarization unit applies an error diffusion matrix in which the diffusion coefficient of a pixel diagonal to a focused-on pixel is greater than those of other pixels.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2016-032958, filed on Feb. 24,2016, the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to an image processing apparatus thatprocesses image data for printing.

Description of the Related Art

In printing a multi-gradation image, the image is subjected tobinarization processing in conformity with a printing apparatus.However, simply binarizing a multi-gradation image causes anintermediate concentration to be lost and thus decreases the imagequality. Hence, various types of binarization processing that allowprevention of a decrease in image quality have been proposed. Errordiffusion methods are widely known as representative techniques.

Error diffusion methods have been improved in various ways. In aconventional error diffusion method, particular textures may begenerated at or around an intermediate concentration. Accordingly, animage processing apparatus has been proposed that adds random noise toimage data so as to make the textures unnoticeable, and that changes athreshold for binarization processing according to the noise so as toprevent the image quality from decreasing due to the noise addition (seepatent document 1).

-   [Patent Document 1] Japanese Laid-open Patent Publication No.    11-328357

SUMMARY OF THE INVENTION

As described above, in the conventional error diffusion processing,textures with links between dots having a regular periodicity aregenerated at or around an intermediate concentration. Particularly inprinting a photographic image, many textures are generated, and thisgreatly decreases the print quality of the photographic image. Toimprove print quality, there is a need for a technique to suppress ageneration of textures in error diffusion processing.

To achieve the object described above, an image processing apparatusthat performs binarization processing of multi-valued image data usingan error diffusion method so as to output the binary image data to animage formation apparatus includes an image processor that performserror diffusion processing by applying a predetermined error diffusionmatrix to multi-valued image data having pixels two-dimensionallyarranged in a main scanning direction that is a direction in which headsare arranged and in a subscanning direction orthogonal to the mainscanning direction, so as to convert the multi-valued image data intobinary image data, wherein the image processor applies, as the errordiffusion matrix, an error diffusion matrix in which the diffusioncoefficient of a pixel diagonal to a focused-on pixel is greater thanthose of the pixels other than the pixel diagonal to the focused-onpixel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a hardware block diagram illustrating an exemplaryconfiguration of an image formation apparatus;

FIG. 2 is a functional block diagram of an image processor;

FIG. 3 is a functional block diagram related to error diffusionprocessing performed by a binarization unit;

FIG. 4 illustrates a pixel arrangement for image data;

FIG. 5 illustrates a correlation between diffusion coefficients;

FIG. 6 illustrates JJN coefficients;

FIG. 7A illustrates a situation in which, for a main scanning direction,the resolution of image data is equal to a head resolution;

FIG. 7B illustrates a situation in which, for a main scanning direction,the resolution of image data is lower than a head resolution;

FIG. 8 illustrates exemplary error diffusion matrixes in accordance withan embodiment;

FIG. 9 illustrates other exemplary error diffusion matrixes inaccordance with an embodiment; and

FIG. 10 illustrates examples of printed images for individual diffusioncoefficients.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following describes an image processing apparatus in accordance withembodiments of the present invention by referring to the drawings. FIG.1 is a hardware block diagram illustrating an exemplary configuration ofan image formation apparatus 1. The present embodiment is describedusing a screen printing apparatus as a specific example of the imageformation apparatus 1. However, the image formation apparatus 1 is notlimited to a screen printing apparatus and may be an inkjet printingapparatus.

A screen printing apparatus melts the film of screen printing paperusing thermal heads so as to form holes through the paper, therebyproviding a printing plate to be used for printing to produce manyduplications. In particular, first, the screen printing apparatusdrives, for example, the thermal heads according to image data obtainedby reading a document with, for example, a scanner so as to bore holesby melting screen printing paper, and performs plate-making processingon the screen printing paper, thereby producing a printing plate. Then,the screen printing apparatus wraps the produced printing plate around aprint drum. In addition, the screen printing apparatus supplies ink fromthe inside of the print drum and performs printing by transferring theink to a print sheet using, for example, a roller.

The image formation apparatus 1 includes a scanner unit 10, an imageprocessor 20, a plate making unit 30, a printing unit 40, a CPU (CentralProcessing unit) 50, a temporary storage unit 60, and a nonvolatilestorage unit 70.

The scanner unit 10 includes a line image sensor that photoelectricallyreads image information of a document. The scanner unit 10 reads thedocument by scanning the document with the line image sensor and outputsmulti-valued monochrome or color image data. The scanner unit 10 may bereferred to as an image reading unit or an image input unit.

The image processor 20 applies, for example, edge enforcement processingand density correction processing to the multi-valued image data outputfrom the scanner unit 10, and performs binarization processing so as tooutput binary data. The entirety of, or portions of, the image processor20 consists of, for example, a gate array. Details of the imageprocessor 20 will be described hereinafter.

The plate making unit 30 performs plate-making processing according toimage data binarized by the image processor 20. The plate making unit 30includes thermal heads that include a plurality of heat generatorsarranged in a line. Using the thermal heads, the plate making unit 30performs plate-making processing of screen printing paper unspooled froma screen printing paper roll. The plate making unit 30 may comprise twoplate making units, a first plate making unit (not illustrated) and asecond plate making unit (not illustrated).

The printing unit 40 performs printing on a print sheet using the screenprinting paper produced by the plate making unit 30. The printing unit40 includes, for example, a paper feed stand in which print sheets arestored (not illustrated), a transportation unit that transports a printsheet (not illustrated), a print drum around which screen printing paperis wrapped (not illustrated), a press roller that presses a print sheetagainst the print drum (not illustrated), and a paper ejection standfrom which a print sheet is ejected after printing (not illustrated).

The CPU 50 reads a control program and comprehensively performs controlprocessing of the image formation apparatus 1 in accordance with theread control program. The temporary storage unit 60 temporarily storesimage data, the control program, and plate-making-processing dataprovided by the plate making unit 30. The temporary storage unit 60 is,for example, a DRAM (Dynamic Random Access Memory).

The nonvolatile storage unit 70 stores a control program, various typesof data, and a table in a nonvolatile manner. The nonvolatile storageunit 70 is, for example, an HDD (Hard Disk Drive) or a flash memory.

FIG. 2 is a functional block diagram of the image processor 20. Theimage processor 20 includes an edge enhancement unit 22, a densitycorrection unit 24, and a binarization unit 26. As described above,multi-valued image data from the scanner unit 10 is input to the imageprocessor 20. The edge enhancement unit 22 applies digital filterprocessing to the input multi-valued image data so as to enhance animage contour. By referring to a γ correction table stored in thenonvolatile storage unit 70, the density correction unit 24 applies, forexample, γ correction processing to the multi-valued image data that hasundergone counter enhancement processing.

The binarization unit 26 applies error diffusion processing to themulti-valued image data that has undergone density correctionprocessing, so as to convert the data into binary data. The imageprocessor 20 outputs the binary data to the plate making unit 30. Errordiffusion processing includes binarizing multi-valued image data bycomparing the data with a predetermined threshold, and diffusing adifference between the input image data and the threshold amongneighboring pixels to subsequently undergo binarization processing.

The plate making unit 30 fabricates a printing plate by driving thethermal heads according to binary data. Details of binarizationprocessing performed by the binarization unit 26 are described withreference to FIG. 3.

FIG. 3 is a functional block diagram related to error diffusionprocessing performed by the binarization unit 26. The binarization unit26 includes an error addition unit 100, a binarization determinationunit 102, an error calculation unit 104, an error diffusion unit 106, amatrix table 108, and an error memory 110. The following specificallydescribes functions of those units in accordance with the processingflow. Assume that image data has 256 gradations of gray (0-255).

FIG. 4 illustrates a pixel arrangement for input image data. As depictedin FIG. 4, image data is arranged two dimensionally in vertical andlateral directions. In FIG. 4, an i direction is the lateral direction,and a j direction is the vertical direction. A pixel is expressed as (i,j). i represents an address for a main scanning direction, and jrepresents an address for a subscanning direction. The main scanningdirection is a direction in which heads of the image formation apparatus1 are arranged in line. The subscanning direction is orthogonal to themain scanning direction. The subscanning direction is also a directionin which a document is transported for reading or a direction in which areading unit is transported.

The binarization unit 26 performs binarization processing for pixels inan order indicated by broken lines in FIG. 4. A pixel to be binarized atthat time is hereinafter referred to as a focused-on pixel and expressedby “*”.

The error addition unit 100 adds error data E(i, j) of a focused-onpixel that has been read from the error memory 110 to multi-valued inputimage data In(i, j) of the focused-on image so as to calculate correctedimage data C(i, j). Input image data In (i, j) is 0 to 255.

-   -   In(i, j)+E(i, j)=C(i, j)

The binarization determination unit 102 compares corrected image dataC(i, j) with a predetermined threshold Th so as to output binary dataOut(i, j). The predetermined threshold Th is, for example, 127. Whencorrected image data C(i, j)>Th, the binarization determination unit 102outputs 255; when corrected image data C(i, j)Th, the binarizationdetermination unit 102 outputs 0.

The error calculation unit 104 calculates a binarization error e(i, j)according to a difference between corrected image data C(i, j) andbinary data Out(i, j).

-   -   C(i, j)−Out(i, j)=e(i, j)

When, for example, C(i, j)=100 holds, Out(i, j)=0 is satisfied accordingto Th=127, leading to binarization error e(i, j)=100.

The error diffusion unit 106 multiplies a diffusion coefficient of anerror diffusion matrix allocated in accordance with the position of aneighboring pixel by binarization error e(i, j), so as to calculateerror data E(i, j) for each pixel. The error diffusion unit 106 reads adiffusion coefficient table for error diffusion matrixes from the matrixtable 108. The matrix table 108 is included in the nonvolatile storageunit 70.

Calculation formulae for error data E(i, j) are indicated below. In thisexample, the size of the error diffusion matrix is 5×3. FIG. 5 depictsan exemplary 5×3 error diffusion matrix, and p1-p12 are diffusioncoefficients of individual pixel positions.

-   -   E(i+1, j)=E(i+1, j)+e(i, j)×p1    -   E(i+2, j)=E(i+2, j)+e(i, j)×p2    -   E(i−2, j+1)=E(i−2, j+1)+e(i, j)×p3    -   E(i−1, j+1)=E(i−1, j+1)+e(i, j)×p4    -   E(i, j+1)=E(i, j+1)+e(i, j)×p5    -   E(i+1, j+1)=E(i+1, j+1)+e(i, j)×p6    -   E(i+2, j+1)=E(i+2, j+1)+e(i, j)×p7    -   E(i−2, j+2)=E(i−2, j+2)+e(i, j)×p8    -   E(i−1, j+2)=E(i−1, j+2)+e(i, j)×p9    -   E(i, j+2)=E(i, j+2)+e(i, j)×p10    -   E(i+1, j+2)=E(i+1, j+2)+e(i, j)×p11    -   E(i+2, j+2)=E(i+2, j+2)+e(i, j)×p12

The error diffusion unit 106 adds the calculated error data E to errordata E of a corresponding position that has been stored in the errormemory. The error memory 110 is included in the temporary storage unit60.

The following describes specific examples of diffusion coefficients ofan error diffusion matrix. Diffusion coefficients of an error diffusionmatrix are described by indicating textures generated by each of thediffusion coefficients. First, a JJN coefficient, which is known in aconventional error diffusion method, is described as a comparativeexample. JJN coefficients are diffusion coefficients of an errordiffusion matrix proposed by Jarvis, Judice and Ninke. FIG. 6 indicatesthe values of JJN coefficients. The orientation of the JJN coefficientsis such that the main scanning direction is a lateral direction, as inFIG. 5. In FIG. 6, only the numerators of coefficients are indicated,and the denominator “1/48” is omitted. “*” represents a focused-onpixel, as described above. As depicted in FIG. 6, the JJN coefficientsare set in a manner such that a greater weight is assigned to pixelscloser to the focused-on pixel.

FIG. 10 illustrates printed images that have undergone binarizationprocessing based on error diffusion processing that includes theconventional JJN coefficients. Each of the printed images in FIG. 10 isan enlargement of a portion of an actual printed image. Printed imagesPt1 and Pt2 are based on the conventional JJN coefficients. The printedimages Pt1 and Pt2 have a difference in resolution of input image data.A printed image Pt3 is based on the embodiment.

FIGS. 7A and 7B illustrate a relationship between the resolution ofimage data and a head resolution for the main scanning direction. Theheads are thermal heads for forming a printing plate but may be inkjetheads. FIG. 7A illustrates a situation in which, for a main scanningdirection, the resolution of image data is equal to a head resolution.This corresponds to, for example, a situation in which both theresolution of image data and the head resolution are 600 dpi (dots perinch). FIG. 7B illustrates a situation in which, for a main scanningdirection, the resolution of image data is lower than a head resolution.This corresponds to, for example, a situation in which the resolution ofimage data is 300 dpi and the head resolution is 600 dpi. When, asdepicted in FIG. 7B, the head resolution of the image formationapparatus is twice the resolution of input image data, the heads aredriving-controlled in a manner such that the heads open for the samedata of two pixels in the main scanning direction (overprinting scheme).

Descriptions are given by referring to FIG. 10 again. The printed imagePt1 is obtained when the resolution of image data and the headresolution are equal to each other for the main scanning direction inthe situation depicted in FIG. 7A. The printed image Pt2 is obtainedwhen the resolution of image data is half the head resolution for themain scanning direction in the situation depicted in FIG. 7B. Texturesparallel to the subscanning direction are generated in both the printedimages Pt1 and Pt2; the textures parallel to the subscanning directionin the printed image Pt2 are larger than those in the printed image Pt1.This is because the textures generated by error diffusion processing inthe subscanning direction in the printed image Pt2 are double printed inthe subscanning direction by overprinting, thereby becoming morenoticeable.

The following describes an error diffusion matrix in accordance with theembodiment. FIGS. 8 and 9 illustrate exemplary error diffusion matrixesin accordance with the embodiment. The error diffusion matrix inaccordance with the embodiment is such that the diffusion coefficientsof pixels diagonal to a focused-on pixel are greater than those of otherpixels. This allows textures that would be generated along thesubscanning direction, among other things, to be decreased.

An error diffusion matrix m1 in FIG. 8 is a first example of the errordiffusion matrix in accordance with the embodiment. The denominator ofeach diffusion coefficient is “1/32”. A pixel indicated by “*” is afocused-on pixel. The diffusion coefficients of pixels q1 diagonal tothe focused-on pixel is “5”, and the diffusion coefficients of pixels q2that are not diagonal to the focused-on pixel are “3” or “1”.

The printed image Pt3 in FIG. 10 is an exemplary image obtained byperforming overprinting by applying the error diffusion matrix m1, i.e.,a matrix in which pixels diagonal to a focused-on pixel have a greatdiffusion coefficient. It is clear that there are far fewer textures inthe subscanning direction in the printed image Pt3 than in the printedimage Pt2. That is, applying error diffusion processing such as theerror diffusion matrix m1 allows textures that would be generated by theconventional JJN-coefficient-based error diffusion processing to beremarkably decreased.

The diffusion coefficients of the error diffusion matrix of theembodiment are not limited to those of the error diffusion matrix m1.Error diffusion matrixes m2 and m3 in FIG. 8 are other exemplary errordiffusion matrixes in which pixels diagonal to a focused-on pixel have agreat diffusion coefficient, and the matrix sizes of the error diffusionmatrixes m2 and m3 are the same as that of the error diffusion matrix m1(5×3). The denominator of diffusion coefficients of the error diffusionmatrixes m2 and m3 are “1/48”. The error diffusion matrix m2 is suchthat the diffusion coefficients of pixels q1 diagonal to a focused-onpixel are “7”, thereby making large the difference from “2” and “4”,which are the diffusion coefficients of pixels q2 that are not diagonalto the focused-on pixel.

By contrast, the error diffusion matrix m3 is such that the diffusioncoefficients of pixels q1 diagonal to a focused-on pixel are “6” andsuch that the diffusion coefficients of pixels q2 that are not diagonalto the focused-on pixel are “2”, “3”, or “4”. The difference betweeneach of the diffusion coefficients of pixels q1 diagonal to a focused-onpixel and each of those of pixels q2 that are not diagonal to thefocused-on pixel is smaller in the diffusion matrix m3 than in the errordiffusion matrix m2.

The error diffusion matrixes m2 and m3 may be stored in the matrix table108 so that the error diffusion unit 106 can select either of thosematrixes depending on conditions. For example, the error diffusion unit106 may select either of the error diffusion matrixes according to acomparison between the resolution of input image data and the headresolution. In particular, when the resolution of image data is lowerthan the head resolution (FIG. 7B), the error diffusion unit 106 may usethe error diffusion matrix m2, which achieves a higher diffusion effect;otherwise, the error diffusion unit 106 may use the error diffusionmatrix m3.

FIG. 9 illustrates other exemplary error diffusion matrixes inaccordance with the embodiment. An error diffusion matrix m4 in FIG. 9has a matrix size of 5×2. The matrix size is not limited to 5×3, andvarious sizes may be selected. The denominators of diffusion matrixesare 1/16.

The error diffusion matrix m5 in FIG. 9 is such that the direction ofbinarization processing is opposite to that for the error diffusionmatrix m2. The error diffusion matrix m4 is such that binarizationprocessing is performed in a direction from the bottom right to the topleft, and this is opposite to the example of FIG. 4.

In each of the error diffusion matrixes m1-m5, an equal value is set asthe diffusion coefficients of pixels diagonal to a focused-on pixel, andthis does not necessarily need to be satisfied. As long as the diffusioncoefficients of pixels diagonal to a focused-on pixel are greater thanthose of the other pixels, any value can be the diffusion coefficientsof the pixels diagonal to the focused-on pixel. For example, the errordiffusion matrix m1 may be arranged in a manner such that the diffusioncoefficients of pixels q1 diagonal to and close to the focused-on pixelare set to “5” and such that the diffusion coefficients of pixels q1diagonal to and distant from the focused-on pixel are set to “4”.

<Effect>

In the embodiment, a greater weight is assigned to diffusioncoefficients in a diagonal direction of an error diffusion matrix so asto diffuse errors more greatly in the diagonal direction, so thattextures can be decreased that would be generated in the subscanningdirection by error diffusion processing. In particular, when theresolution of image data is lower than a head resolution, there has beena problem of generation of large textures in the subscanning directiondue to overprinting because a print making unit double prints textures(overprinting) in the main scanning direction; however, the errordiffusion matrix in accordance with the embodiment can decrease texturegeneration remarkably.

<Variation>

It has been stated that the image processor 20 is achieved byhardware-based processing, but portions of, or the entirety of, theimage processor 20 may be achieved by software processing performed by aCPU that has read a control program.

In the described examples, the image processor 20 has been incorporatedinto the image formation apparatus 1. However, the image processor 20may be separated from the image formation apparatus 1.

In the described examples, multi-valued image data from the scanner unit10 is input to the image processor 20. However, multi-valued image datafrom an information processing terminal such as a PC (Personal Computer)may be transmitted to the image processor 20.

In the meantime, the present invention is not limited to the abovementioned embodiments as they are, but may be embodied in practicaluses, by modifying components without departing from a gist of theembodiments. Further, by appropriately combining a plurality ofcomponents disclosed in the above mentioned embodiments, variousinventions may be configured. For example, all of the componentsdisclosed in the embodiments may be combined as appropriate. Further,the components may be combined appropriately over different embodiments.Within a scope of the invention that does not depart from the gist ofthe invention, various modifications or applications are possible, as amatter of course.

EXPLANATIONS OF LETTERS OR NUMERALS

-   1: Image formation apparatus-   10: Scanner unit-   20: Image processor    -   20: Edge enhancement unit    -   24: Density correction unit    -   26: Binarization unit-   30: Plate making unit-   40: Printing unit-   50: CPU-   60: Temporary storage unit-   70: Nonvolatile storage unit-   100: Error addition unit-   102: Binarization determination unit-   104: Error calculation unit-   106: Error diffusion unit-   108: Matrix table-   110: Error memory

What is claimed is:
 1. An image processing apparatus that performsbinarization processing of multi-valued image data using an errordiffusion method so as to output binary image data to an image formationapparatus, the image processing apparatus comprising: an image processorthat performs error diffusion processing by applying a predeterminederror diffusion matrix to multi-valued image data having pixelstwo-dimensionally arranged in a main scanning direction that is adirection in which heads are arranged and in a subscanning directionorthogonal to the main scanning direction, so as to convert themulti-valued image data into binary image data, wherein the imageprocessor applies, as the error diffusion matrix, an error diffusionmatrix in which a diffusion coefficient of a pixel diagonal to afocused-on pixel is greater than diffusion coefficients of pixels otherthan the pixel diagonal to the focused-on pixel.
 2. The image processingapparatus according to claim 1, wherein the image processor applies afirst error diffusion matrix as the error diffusion matrix when a headresolution is equal to a resolution of image data for the subscanningdirection, applies a second error diffusion matrix as the errordiffusion matrix when the resolution of the image data is less than thehead resolution for the main scanning direction, the second diffusionmatrix having a larger difference between a diffusion coefficient of apixel diagonal to a focused-on pixel and a diffusion coefficient of eachof other pixels than the first error diffusion matrix.